Week 11 Flashcards
Cause Density Bias
- Tendency to overestimate relationship in cue and outcome
- Usually when the cue/cause happens frequently
Causation
A cause to a situation appears to exist but often does not
Outcome Density Bias
- Tendency to overestimate relationship in cue and outcome
- Usually when the cue/outcome happens frequently
Illusory Causal Judgement Errors
- Once illusory causal judgement is established it can be difficult to replace
- Even when new information explains outcomes better.
- Yarritu et al, 2015
Representativeness Heuristic
- Representativeness is when a characteristic is associated with another cue
E.g. Robert is quiet and wears glasses; he is probably an accountant not a surfer
Base Rate
Relative proportion of different classes in the population
Base Rate Neglect
- When presented with base rate information people guess odds correctly
- Adding descriptions can increase estimates that are not correct
Inverse Base Rate Effect
- Medin & Edelson 1988
- Participants asked to diagnose disease based on symptom of pairs
- A & B had disease 01, A & C had disease 02
- Disease 01 was common & 02 was rare
- After learning contingencies asked to diagnose
- Tended to diagnose rare disease even though it was less likely
Conjunction Rule
The probability of two connected events/features cannot be higher than it’s single features
Law of Large Numbers
The larger the number of individuals chosen from a population the more they will come to represent a population
e.g. Small/Large Hospital
Myside Bias
Tendency for people to test and measure ideas based on their own opinions
Lord & Coworkers 1979
Confirmation Bias
Tendency to select information that already agrees with what we believe.
Overlooking information that contradicts it
Wason, 1960
Evaluating False Evidence
- Even if your reasoning has no error you can still come to the wrong conclusion if your facts are wrong
- People don’t usually evaluate evidence and rely heavily on inaccurate information
- Wineburg et al 2016
- Flowers with nuclear birth defects from Japan
The Backfire Effect
- Nyhan & Reifler 2010
- Study of misperception about Iraq’s Weapons of Mass Destruction
- Subjects given false news about WMD then some were corrected confirming no WMD
- Subjects made decisions differently depending on political affiliation
- Also differed if they had been corrected or not
- With very conservative’s political views belief became stronger even if they were corrected
Availability Heuristic
- Events more easily remembered are judged as more probable
- Occurs when easily remembered event is less probable
Illusory Correlation
- Strong correlation between two events appears but does not exist
- Happens when there is no/weak correlation
Representativeness Heuristic
- Probability that A is connected to B determined by how features of A resemble B
- Presence of similar properties don’t predict membership of class B
Base Rate
- Relative proportions of different types in a population
- When not taken into account errors in judgement occur
Conjunction Rate
- Probability of two features combined cannot be higher than each single feature
- Happens when higher probability is given to a conjunction pair
Law of Large Numbers
The more individuals chosen from a population, the more likely the sample represents the whole group.
Assumed when small number is taken and thought to be widespread
Myside Bias
- People tend to evaluate evidence based on their own beliefs
- A type of confirmation bias
- Happens when there is a narrow focus only on confirming information
Confirmation Bias
- Selectively looking for information that confirms belief
- Dismissing information that contradicts it
Backfire Effect
- Support for a viewpoint becomes stronger even if given facts the oppose their viewpoints
- Happens when people hold to their beliefs in the face of contradictory evidence
Judgement
The process of forming an opinion or conclusion
Inductive Reasoning
- Based on observation or reaching conclusions from evidence
- Conclusions are probably true, but not definitely true
- Science is based on Inductive Reasoning
- Have an idea
- Collect Data
- Draw a conclusion
Define Heuristics
- Rules of thumb likely to provide correct answer to a problem
- Are not Fool-Proof
- Availability Heuristic
- Representativeness Heuristic
Over Reliance on Heuristics can Lead To
- Illusory correlations and stereotypes
- Illusory causations
- Incorrect judgment of base rate
Cognitive Biases Could
- Hinder our abillity to gather evidence
- Distort our Judgement
- Can cause:
- Myside Bias
- Confirmation Bias
- Backfire Effect
Deductive Reasoning
Determine whether a conclusion logically follows from the premise
Syllogism
- Two statements called a premise by a third statement called a conclusion
- Premise 1 - All birds are Animals (All A are B)
- Premise 2 - All animals eat food (All B are C)
- Conclusion: Therefore, all birds eat food (All A are C)
Syllogisms are . . .
Valid if the conclusion follows logically for the premise
If two premise are true the conclusion must be true
True is hard to define but we assume it matches reality
Syllogism Validity
- Syllogisms can be invalid even when each premise and the conclusion seem reasonable.
- Is the reasoning behind syllogism 3 valid?
Belief Bias
Tendency to think a syllogism is valid if it’s conclusion is believable
Mental Model
- A specific situation presented in the mind
- Used to determine the validity of syllogisms
- Uses deductive reasoning
- Create Model of Situation
- Generate Tentative Conclusions about the Model
- Look for Exceptions to Falsify Model
- If no more exceptions exist determine validity of syllogism
Mental Model for Syllogisms
Create Model of a situation
Generate tentative conclusions about the model
Look for exceptions to falsify the model
Conditional Syllogisms
Have two Premises and one Conclusion
But first Premise has an “if __ , then __” form
Modus Ponens
- Latin for “the way that affirms by affirming”
- The conclusion follows logically from the two premises
Conditional Syllogism - If I study, I’ll get a good grade. I studied. Therefore, I’ll get a good grade
Modus Tollens
Latin for “the way that denies by denying”
Conditional Syllogism 2 - If I study, I’ll get a good grade. I didn’t get a good grade. Therefore, I didn’t study.
Affirmation of the Consequent
- Conditional Syllogism 3 - If I study, I’ll get a good grade. I got a good grade. Therefore, I studied
- The conclusion is not valid because even if you didn’t study it is possible to get a good grade.
- There are other factors that can influence a conclusion
Denial of the Antecedent
the conclusion of the syllogism is not valid
e.g. If I study, I’ll get a good grade. I didn’t study. Therefore, I didn’t get a good grade.
Wason Four-Card Problem
- Effect of using real-word items for conditional reasoning
- Determine minimum number of cards to turn over to test
Falsification Principle
To test a rule it is necessary to look for situations that would falsify the rule
Real World - Four Card Problem
Performance improves in real-world terms
Griggs & Cox 1982
Wason Four-Card Problem Results
- 73% provided correct response
- 0 who answered the abstract condition correctly
- Involves familiar regulations
- Griggs & Cox 1982
- Schema of permission used to focus on cards needed to test schema
- People are on the lookout for cheaters
- Thinks awareness is due to evolutionary advantage (1992)
Can Non Humans Reason?
- Research has tried to teach animals to use language
- Premack 1983
- Language trained chimps can succeed with reasoning tasks that untrained ones cannot
- Untrained chimps use only imaginal code - Visual properties of objects
- Language Trained chimps used an abstract code
- Using abstract code can help chimps reason
Not all Reasoning Tasks Require Abstract Code
- Language trained and untrained chimps reliably chose correct cone with the fruit in it
- Prior language training is not important for this task
- Same result is found with children over 4 years of age
Reasoning and Abstract Task
- Premack 1983
- Ability to solve analogy problems assessed by offering various alternatives.
- Imaginal Code not enough for solving analogies because they not based on physical similarities
Chimps Solving Analogies
- Chimps appear to require language to solve analogies
- Language may be required to understand abstract concepts
- Language may just make chimps to be better at taking tests
- Premack said only primates can learn an abstract code
- Difficult to test especially when non primates are learning language
Deductive Reasoning Involves
Determining whether a conclusion logically follows
Syllogism Consists . . .
Two premises followed by a conclusion
Reader needs to determine if it is True an Valid
Mental Model Syllogism
- Helps us validate a syllogism
- Categorical Syllogism
- Conditional Syllogism
Belief Bias
Tendency for people to think a syllogism is valid if it’s conclusion is believable
Watson Four-Card Problem helps . . .
Demonstrate several syllogisms in real world conditions
Premack 1983 Claims
Language is a requirement for abstract thought
Errors in Causal Judgement
- Icecream + Drowning Analogy
- Based on strategic thought involving prior knowledge.
- Perception of one event causing another event to happen.
- This perception is incorrect
What is Representativeness Heuristic
- A mental shortcut that we use when estimating probabilities
e. g. Investors assume a good company will be a good investment
Availability Heuristic
Tendency to use information that comes to mind quickly and easily when making decisions about the future.
Cognitive Reflection
- Ability to reflect on a question and resist reporting the first response that comes to mind
- Overcomes Gut Instinct
Deductive Reasoning Needs to . . .
- Find evidence that disproves a rule
- People avoid this and focus on evidence that supports their idea
- Try instead to ask questions that have a NO answer to falsify a claim
Factors to Strengthen Inductive Reasoning
- Representativeness of observations
- Number of observations
- Quality of evidence
- We use Inductive Reasoning every day
- When we make predictions about what will happen or has happened
e.g. when deciding to settle a child, can try many strategies based on training & experience
Stereotypes
- Oversimplified generalisation about a population
- Usually only focuses on the negative
- Characteristics of a group lead to focus on behaviours associated with that group
- Could be due to selective attention makes behaviours “more available”
- Chapman & Chapman 1969
- Hamilton 1981
*
Used for Making Judgements
- Assimilation
- Accommodation
- Biased Assimilation
Assimilation
- Fitting new data within an existing practice
- That is new person/situation
- Melding stereotypes about that situation
Accommodation
- Changing stereotypes to incorporate the new data
- We resist change of our stereotypes
- It’s easier to assimilate than accommodate
Biased Assimilation
We bend new experiences to fit in with our existing stereotypes
Base Rate Neglect
- When people are given a base rate for a population they usually estimate probability correctly
- When given more information they tend to increase odds based on stereotypes
Inverse Base Rate
- Subjects asked to diagnose disease based on symptom pairs
- Sometimes we pay attention to rarer outcomes
- This caused subjects to choose D02 even though it was considered rare.
Inverse Base Rate Rules
- When shown A&C or A&B people make the right choices because they fit the rule
- BUT when shown B&C together it does not fit the rule
- People choose the option that does not fit the rule because we tend to notice what is rare
- Known in medical field “diagnosing a zebra”
What is the difference between Deductive Reasoning and Inductive Reasoning?
- Deductive
- Determine whether a conclusion logically follows from the premise
- Find evidence to prove the rule
- Inductive
- Based on observations and gathering evidence
- Observe, collect data, draw a conclusion
Belief Bias
- If the conclusion of a Syllogism is reasonable we tend to think its true.
- Here B = People, which could mean students
- But B = People might not all be students
- Therefore example is not valid
Categorical Syllogism
We describe the relation between two categories using “All, No, or Some”
